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164 lines
6.6 KiB
164 lines
6.6 KiB
import math
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from tinygrad.tensor import Tensor
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from tinygrad.nn import BatchNorm2d
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from tinygrad.helpers import get_child, fetch
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from tinygrad.nn.state import torch_load
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class MBConvBlock:
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def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se, track_running_stats=True):
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oup = expand_ratio * input_filters
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if expand_ratio != 1:
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self._expand_conv = Tensor.glorot_uniform(oup, input_filters, 1, 1)
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self._bn0 = BatchNorm2d(oup, track_running_stats=track_running_stats)
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else:
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self._expand_conv = None
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self.strides = strides
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if strides == (2,2):
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self.pad = [(kernel_size-1)//2-1, (kernel_size-1)//2]*2
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else:
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self.pad = [(kernel_size-1)//2]*4
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self._depthwise_conv = Tensor.glorot_uniform(oup, 1, kernel_size, kernel_size)
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self._bn1 = BatchNorm2d(oup, track_running_stats=track_running_stats)
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self.has_se = has_se
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if self.has_se:
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num_squeezed_channels = max(1, int(input_filters * se_ratio))
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self._se_reduce = Tensor.glorot_uniform(num_squeezed_channels, oup, 1, 1)
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self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
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self._se_expand = Tensor.glorot_uniform(oup, num_squeezed_channels, 1, 1)
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self._se_expand_bias = Tensor.zeros(oup)
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self._project_conv = Tensor.glorot_uniform(output_filters, oup, 1, 1)
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self._bn2 = BatchNorm2d(output_filters, track_running_stats=track_running_stats)
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def __call__(self, inputs):
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x = inputs
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if self._expand_conv is not None:
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x = self._bn0(x.conv2d(self._expand_conv)).swish()
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x = x.conv2d(self._depthwise_conv, padding=self.pad, stride=self.strides, groups=self._depthwise_conv.shape[0])
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x = self._bn1(x).swish()
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if self.has_se:
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x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4])
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x_squeezed = x_squeezed.conv2d(self._se_reduce, self._se_reduce_bias).swish()
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x_squeezed = x_squeezed.conv2d(self._se_expand, self._se_expand_bias)
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x = x.mul(x_squeezed.sigmoid())
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x = self._bn2(x.conv2d(self._project_conv))
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if x.shape == inputs.shape:
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x = x.add(inputs)
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return x
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class EfficientNet:
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def __init__(self, number=0, classes=1000, has_se=True, track_running_stats=True, input_channels=3, has_fc_output=True):
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self.number = number
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global_params = [
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# width, depth
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(1.0, 1.0), # b0
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(1.0, 1.1), # b1
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(1.1, 1.2), # b2
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(1.2, 1.4), # b3
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(1.4, 1.8), # b4
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(1.6, 2.2), # b5
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(1.8, 2.6), # b6
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(2.0, 3.1), # b7
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(2.2, 3.6), # b8
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(4.3, 5.3), # l2
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][max(number,0)]
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def round_filters(filters):
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multiplier = global_params[0]
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divisor = 8
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filters *= multiplier
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new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
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if new_filters < 0.9 * filters: # prevent rounding by more than 10%
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new_filters += divisor
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return int(new_filters)
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def round_repeats(repeats):
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return int(math.ceil(global_params[1] * repeats))
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out_channels = round_filters(32)
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self._conv_stem = Tensor.glorot_uniform(out_channels, input_channels, 3, 3)
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self._bn0 = BatchNorm2d(out_channels, track_running_stats=track_running_stats)
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blocks_args = [
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[1, 3, (1,1), 1, 32, 16, 0.25],
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[2, 3, (2,2), 6, 16, 24, 0.25],
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[2, 5, (2,2), 6, 24, 40, 0.25],
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[3, 3, (2,2), 6, 40, 80, 0.25],
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[3, 5, (1,1), 6, 80, 112, 0.25],
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[4, 5, (2,2), 6, 112, 192, 0.25],
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[1, 3, (1,1), 6, 192, 320, 0.25],
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]
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if self.number == -1:
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blocks_args = [
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[1, 3, (2,2), 1, 32, 40, 0.25],
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[1, 3, (2,2), 1, 40, 80, 0.25],
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[1, 3, (2,2), 1, 80, 192, 0.25],
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[1, 3, (2,2), 1, 192, 320, 0.25],
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]
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elif self.number == -2:
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blocks_args = [
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[1, 9, (8,8), 1, 32, 320, 0.25],
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]
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self._blocks = []
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for num_repeats, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio in blocks_args:
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input_filters, output_filters = round_filters(input_filters), round_filters(output_filters)
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for n in range(round_repeats(num_repeats)):
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self._blocks.append(MBConvBlock(kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se=has_se, track_running_stats=track_running_stats))
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input_filters = output_filters
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strides = (1,1)
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in_channels = round_filters(320)
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out_channels = round_filters(1280)
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self._conv_head = Tensor.glorot_uniform(out_channels, in_channels, 1, 1)
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self._bn1 = BatchNorm2d(out_channels, track_running_stats=track_running_stats)
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if has_fc_output:
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self._fc = Tensor.glorot_uniform(out_channels, classes)
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self._fc_bias = Tensor.zeros(classes)
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else:
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self._fc = None
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def forward(self, x):
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x = self._bn0(x.conv2d(self._conv_stem, padding=(0,1,0,1), stride=2)).swish()
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x = x.sequential(self._blocks)
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x = self._bn1(x.conv2d(self._conv_head)).swish()
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x = x.avg_pool2d(kernel_size=x.shape[2:4])
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x = x.reshape(shape=(-1, x.shape[1]))
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return x.linear(self._fc, self._fc_bias) if self._fc is not None else x
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def load_from_pretrained(self):
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model_urls = {
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0: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth",
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1: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth",
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2: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth",
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3: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth",
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4: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth",
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5: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth",
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6: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth",
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7: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth"
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}
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b0 = torch_load(fetch(model_urls[self.number]))
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for k,v in b0.items():
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if k.endswith("num_batches_tracked"): continue
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for cat in ['_conv_head', '_conv_stem', '_depthwise_conv', '_expand_conv', '_fc', '_project_conv', '_se_reduce', '_se_expand']:
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if cat in k:
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k = k.replace('.bias', '_bias')
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k = k.replace('.weight', '')
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#print(k, v.shape)
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mv:Tensor = get_child(self, k)
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vnp = v #.astype(np.float32)
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vnp = vnp if k != '_fc' else vnp.T
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#vnp = vnp if vnp.shape != () else np.array([vnp])
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if mv.shape == vnp.shape:
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mv.replace(vnp.to(mv.device))
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else:
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print("MISMATCH SHAPE IN %s, %r %r" % (k, mv.shape, vnp.shape))
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